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Programs / AI Cloud & MLOps EngineerAdvanced PATHWAY

Operate AI at Production Scale

Cloud engineers and infrastructure professionals. Learn to build systems with hands-on projects and applied engineering.

PROGRAM OVERVIEW

AI Cloud & MLOps Engineer

Operate AI at Production Scale. Develop practical competencies through focused labs, project work and a final capstone.

Who this program is for

Cloud engineers and infrastructure professionals.

Career alignment

MLOps Engineer, AI Infrastructure Engineer. Role alignment describes relevant skills and does not guarantee employment.

YOUR LEARNING BLUEPRINT

From understanding
to implementation.

01Cloud Platforms+

Skills and technologies you’ll develop:

AWSAzureGCP

Hands-on lab: apply these concepts, test the results and document engineering decisions.

02Deployment Infrastructure+

Skills and technologies you’ll develop:

DockerKubernetesGPU InfrastructureModel Serving

Hands-on lab: apply these concepts, test the results and document engineering decisions.

03Delivery & Security+

Skills and technologies you’ll develop:

CI/CDAI GatewaysSecrets Management

Hands-on lab: apply these concepts, test the results and document engineering decisions.

04Observability+

Skills and technologies you’ll develop:

LoggingObservabilityScalingLoad Balancing

Hands-on lab: apply these concepts, test the results and document engineering decisions.

05Resilience & Economics+

Skills and technologies you’ll develop:

Cost OptimizationDisaster RecoveryProduction AI Operations

Hands-on lab: apply these concepts, test the results and document engineering decisions.

Show what you can build.

PROJECT 1

Containerized model service

Build a working system with defined requirements, evaluation and documented limitations.

PROJECT 2

Deployment pipeline

Build a working system with defined requirements, evaluation and documented limitations.

PROJECT 3

Operations dashboard

Build a working system with defined requirements, evaluation and documented limitations.

FINAL CAPSTONE

Demonstrate practical competency.

Deploy an observable AI service with resilience, scaling and a cost management plan.

Before you begin.

Prerequisites

Linux, Docker and basic cloud experience.

Professional credential

AI MLOps Engineer Professional Certificate. Completion requires published coursework, assessments, projects and capstone requirements. Final passing scores, exam attempts and grading policies will be confirmed for your cohort before enrollment. This is an Epic IT certificate, not a university degree or third-party accredited qualification.

Career pathway

Build on this program with a related engineering pathway or industry specialization.

Questions, answered.

How long is this program?

Schedule and duration to be confirmed.

What is the delivery format?

Online, live and in-person options subject to cohort availability.

What is tuition?

Tuition information coming soon. Pricing, payment plans, start dates and enrollment deadlines will be confirmed before purchase.

What technology do I need?

A reliable computer, internet access and the ability to run development tools. Cloud/API requirements and additional costs will be disclosed in the cohort syllabus.

Is employment guaranteed?

No. Programs develop competencies and portfolio evidence; employment depends on individual preparation and market conditions.

Keep building.

Apply Now